Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations1095
Missing cells104
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory730.3 KiB
Average record size in memory683.0 B

Variable types

Categorical7
Text3
Numeric7

Alerts

batting_avg is highly overall correlated with runs and 1 other fieldsHigh correlation
result is highly overall correlated with winnerHigh correlation
runs is highly overall correlated with batting_avgHigh correlation
team2 is highly overall correlated with toss_winnerHigh correlation
toss_winner is highly overall correlated with team2High correlation
wickets is highly overall correlated with batting_avgHigh correlation
winner is highly overall correlated with resultHigh correlation
match_type is highly imbalanced (78.9%) Imbalance
batting_avg has 26 (2.4%) missing values Missing
runs has 26 (2.4%) missing values Missing
bowling_avg has 26 (2.4%) missing values Missing
wickets has 26 (2.4%) missing values Missing
bowling_avg has 303 (27.7%) zeros Zeros
wickets has 382 (34.9%) zeros Zeros

Reproduction

Analysis started2025-03-14 14:40:39.935311
Analysis finished2025-03-14 14:40:44.546882
Duration4.61 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

match_type
Categorical

Imbalance 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size59.1 KiB
League
1029 
Qualifier
 
28
Eliminator
 
21
Final
 
17

Length

Max length10
Median length6
Mean length6.1378995
Min length5

Characters and Unicode

Total characters6721
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLeague
2nd rowLeague
3rd rowLeague
4th rowLeague
5th rowLeague

Common Values

ValueCountFrequency (%)
League 1029
94.0%
Qualifier 28
 
2.6%
Eliminator 21
 
1.9%
Final 17
 
1.6%

Length

2025-03-14T09:40:44.672678image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:40:44.747010image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
league 1029
94.0%
qualifier 28
 
2.6%
eliminator 21
 
1.9%
final 17
 
1.6%

Most occurring characters

ValueCountFrequency (%)
e 2086
31.0%
a 1095
16.3%
u 1057
15.7%
L 1029
15.3%
g 1029
15.3%
i 115
 
1.7%
l 66
 
1.0%
r 49
 
0.7%
n 38
 
0.6%
Q 28
 
0.4%
Other values (6) 129
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5626
83.7%
Uppercase Letter 1095
 
16.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2086
37.1%
a 1095
19.5%
u 1057
18.8%
g 1029
18.3%
i 115
 
2.0%
l 66
 
1.2%
r 49
 
0.9%
n 38
 
0.7%
f 28
 
0.5%
m 21
 
0.4%
Other values (2) 42
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
L 1029
94.0%
Q 28
 
2.6%
E 21
 
1.9%
F 17
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 6721
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2086
31.0%
a 1095
16.3%
u 1057
15.7%
L 1029
15.3%
g 1029
15.3%
i 115
 
1.7%
l 66
 
1.0%
r 49
 
0.7%
n 38
 
0.6%
Q 28
 
0.4%
Other values (6) 129
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6721
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2086
31.0%
a 1095
16.3%
u 1057
15.7%
L 1029
15.3%
g 1029
15.3%
i 115
 
1.7%
l 66
 
1.0%
r 49
 
0.7%
n 38
 
0.6%
Q 28
 
0.4%
Other values (6) 129
 
1.9%

venue
Text

Distinct58
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size82.1 KiB
2025-03-14T09:40:44.943553image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length69
Median length45
Mean length27.638356
Min length8

Characters and Unicode

Total characters30264
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM Chinnaswamy Stadium
2nd rowPunjab Cricket Association Stadium, Mohali
3rd rowFeroz Shah Kotla
4th rowWankhede Stadium
5th rowEden Gardens
ValueCountFrequency (%)
stadium 852
21.2%
cricket 250
 
6.2%
international 141
 
3.5%
association 122
 
3.0%
wankhede 118
 
2.9%
gardens 93
 
2.3%
eden 93
 
2.3%
ma 85
 
2.1%
chidambaram 85
 
2.1%
mumbai 82
 
2.0%
Other values (96) 2090
52.1%
2025-03-14T09:40:45.413161image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3961
 
13.1%
2916
 
9.6%
i 2430
 
8.0%
t 1847
 
6.1%
d 1652
 
5.5%
n 1594
 
5.3%
e 1463
 
4.8%
r 1317
 
4.4%
u 1308
 
4.3%
m 1306
 
4.3%
Other values (44) 10470
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22489
74.3%
Uppercase Letter 4316
 
14.3%
Space Separator 2916
 
9.6%
Other Punctuation 528
 
1.7%
Dash Punctuation 15
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3961
17.6%
i 2430
10.8%
t 1847
8.2%
d 1652
 
7.3%
n 1594
 
7.1%
e 1463
 
6.5%
r 1317
 
5.9%
u 1308
 
5.8%
m 1306
 
5.8%
h 1105
 
4.9%
Other values (15) 4506
20.0%
Uppercase Letter
ValueCountFrequency (%)
S 1227
28.4%
C 582
13.5%
M 450
 
10.4%
A 372
 
8.6%
D 184
 
4.3%
G 184
 
4.3%
I 167
 
3.9%
P 162
 
3.8%
R 137
 
3.2%
W 126
 
2.9%
Other values (14) 725
16.8%
Other Punctuation
ValueCountFrequency (%)
, 461
87.3%
. 60
 
11.4%
' 7
 
1.3%
Space Separator
ValueCountFrequency (%)
2916
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26805
88.6%
Common 3459
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3961
14.8%
i 2430
 
9.1%
t 1847
 
6.9%
d 1652
 
6.2%
n 1594
 
5.9%
e 1463
 
5.5%
r 1317
 
4.9%
u 1308
 
4.9%
m 1306
 
4.9%
S 1227
 
4.6%
Other values (39) 8700
32.5%
Common
ValueCountFrequency (%)
2916
84.3%
, 461
 
13.3%
. 60
 
1.7%
- 15
 
0.4%
' 7
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3961
 
13.1%
2916
 
9.6%
i 2430
 
8.0%
t 1847
 
6.1%
d 1652
 
5.5%
n 1594
 
5.3%
e 1463
 
4.8%
r 1317
 
4.4%
u 1308
 
4.3%
m 1306
 
4.3%
Other values (44) 10470
34.6%

team1
Categorical

Distinct19
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size71.9 KiB
Royal Challengers Bangalore
135 
Chennai Super Kings
128 
Mumbai Indians
123 
Kolkata Knight Riders
121 
Rajasthan Royals
101 
Other values (14)
487 

Length

Max length27
Median length22
Mean length18.100457
Min length12

Characters and Unicode

Total characters19820
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoyal Challengers Bangalore
2nd rowKings XI Punjab
3rd rowDelhi Daredevils
4th rowMumbai Indians
5th rowKolkata Knight Riders

Common Values

ValueCountFrequency (%)
Royal Challengers Bangalore 135
12.3%
Chennai Super Kings 128
11.7%
Mumbai Indians 123
11.2%
Kolkata Knight Riders 121
11.1%
Rajasthan Royals 101
9.2%
Kings XI Punjab 92
8.4%
Sunrisers Hyderabad 86
7.9%
Delhi Daredevils 85
7.8%
Delhi Capitals 41
 
3.7%
Deccan Chargers 39
 
3.6%
Other values (9) 144
13.2%

Length

2025-03-14T09:40:45.531261image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 251
 
9.2%
super 151
 
5.6%
royal 144
 
5.3%
challengers 144
 
5.3%
bangalore 135
 
5.0%
chennai 128
 
4.7%
delhi 126
 
4.6%
indians 123
 
4.5%
mumbai 123
 
4.5%
punjab 123
 
4.5%
Other values (26) 1271
46.7%

Most occurring characters

ValueCountFrequency (%)
a 2296
 
11.6%
n 1659
 
8.4%
1624
 
8.2%
e 1443
 
7.3%
i 1337
 
6.7%
s 1296
 
6.5%
r 1115
 
5.6%
l 1057
 
5.3%
g 727
 
3.7%
h 666
 
3.4%
Other values (28) 6600
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15385
77.6%
Uppercase Letter 2811
 
14.2%
Space Separator 1624
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2296
14.9%
n 1659
10.8%
e 1443
9.4%
i 1337
8.7%
s 1296
8.4%
r 1115
 
7.2%
l 1057
 
6.9%
g 727
 
4.7%
h 666
 
4.3%
u 619
 
4.0%
Other values (12) 3170
20.6%
Uppercase Letter
ValueCountFrequency (%)
K 507
18.0%
R 481
17.1%
C 352
12.5%
S 251
8.9%
D 250
8.9%
I 215
7.6%
P 160
 
5.7%
B 144
 
5.1%
M 123
 
4.4%
X 92
 
3.3%
Other values (5) 236
8.4%
Space Separator
ValueCountFrequency (%)
1624
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18196
91.8%
Common 1624
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2296
 
12.6%
n 1659
 
9.1%
e 1443
 
7.9%
i 1337
 
7.3%
s 1296
 
7.1%
r 1115
 
6.1%
l 1057
 
5.8%
g 727
 
4.0%
h 666
 
3.7%
u 619
 
3.4%
Other values (27) 5981
32.9%
Common
ValueCountFrequency (%)
1624
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2296
 
11.6%
n 1659
 
8.4%
1624
 
8.2%
e 1443
 
7.3%
i 1337
 
6.7%
s 1296
 
6.5%
r 1115
 
5.6%
l 1057
 
5.3%
g 727
 
3.7%
h 666
 
3.4%
Other values (28) 6600
33.3%

team2
Categorical

High correlation 

Distinct19
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
Mumbai Indians
138 
Kolkata Knight Riders
130 
Rajasthan Royals
120 
Chennai Super Kings
110 
Royal Challengers Bangalore
105 
Other values (14)
492 

Length

Max length27
Median length22
Mean length17.767123
Min length12

Characters and Unicode

Total characters19455
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKolkata Knight Riders
2nd rowChennai Super Kings
3rd rowRajasthan Royals
4th rowRoyal Challengers Bangalore
5th rowDeccan Chargers

Common Values

ValueCountFrequency (%)
Mumbai Indians 138
12.6%
Kolkata Knight Riders 130
11.9%
Rajasthan Royals 120
11.0%
Chennai Super Kings 110
10.0%
Royal Challengers Bangalore 105
9.6%
Kings XI Punjab 98
8.9%
Sunrisers Hyderabad 96
8.8%
Delhi Daredevils 76
6.9%
Delhi Capitals 50
 
4.6%
Deccan Chargers 36
 
3.3%
Other values (9) 136
12.4%

Length

2025-03-14T09:40:45.616391image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 233
 
8.7%
indians 138
 
5.1%
mumbai 138
 
5.1%
super 131
 
4.9%
knight 130
 
4.8%
riders 130
 
4.8%
kolkata 130
 
4.8%
delhi 126
 
4.7%
punjab 123
 
4.6%
rajasthan 120
 
4.5%
Other values (26) 1284
47.9%

Most occurring characters

ValueCountFrequency (%)
a 2301
 
11.8%
n 1607
 
8.3%
1588
 
8.2%
i 1364
 
7.0%
s 1325
 
6.8%
e 1315
 
6.8%
r 1056
 
5.4%
l 953
 
4.9%
g 653
 
3.4%
h 640
 
3.3%
Other values (28) 6653
34.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15086
77.5%
Uppercase Letter 2781
 
14.3%
Space Separator 1588
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2301
15.3%
n 1607
10.7%
i 1364
9.0%
s 1325
8.8%
e 1315
8.7%
r 1056
 
7.0%
l 953
 
6.3%
g 653
 
4.3%
h 640
 
4.2%
u 621
 
4.1%
Other values (12) 3251
21.5%
Uppercase Letter
ValueCountFrequency (%)
K 507
18.2%
R 497
17.9%
C 307
11.0%
S 243
8.7%
D 238
8.6%
I 236
8.5%
P 162
 
5.8%
M 138
 
5.0%
B 111
 
4.0%
X 98
 
3.5%
Other values (5) 244
8.8%
Space Separator
ValueCountFrequency (%)
1588
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17867
91.8%
Common 1588
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2301
 
12.9%
n 1607
 
9.0%
i 1364
 
7.6%
s 1325
 
7.4%
e 1315
 
7.4%
r 1056
 
5.9%
l 953
 
5.3%
g 653
 
3.7%
h 640
 
3.6%
u 621
 
3.5%
Other values (27) 6032
33.8%
Common
ValueCountFrequency (%)
1588
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19455
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2301
 
11.8%
n 1607
 
8.3%
1588
 
8.2%
i 1364
 
7.0%
s 1325
 
6.8%
e 1315
 
6.8%
r 1056
 
5.4%
l 953
 
4.9%
g 653
 
3.4%
h 640
 
3.3%
Other values (28) 6653
34.2%

toss_winner
Categorical

High correlation 

Distinct19
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size71.6 KiB
Mumbai Indians
143 
Kolkata Knight Riders
122 
Chennai Super Kings
122 
Rajasthan Royals
120 
Royal Challengers Bangalore
113 
Other values (14)
475 

Length

Max length27
Median length22
Mean length17.83105
Min length12

Characters and Unicode

Total characters19525
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoyal Challengers Bangalore
2nd rowChennai Super Kings
3rd rowRajasthan Royals
4th rowMumbai Indians
5th rowDeccan Chargers

Common Values

ValueCountFrequency (%)
Mumbai Indians 143
13.1%
Kolkata Knight Riders 122
11.1%
Chennai Super Kings 122
11.1%
Rajasthan Royals 120
11.0%
Royal Challengers Bangalore 113
10.3%
Sunrisers Hyderabad 88
8.0%
Kings XI Punjab 85
7.8%
Delhi Daredevils 80
7.3%
Delhi Capitals 50
 
4.6%
Deccan Chargers 43
 
3.9%
Other values (9) 129
11.8%

Length

2025-03-14T09:40:45.699819image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 231
 
8.6%
mumbai 143
 
5.3%
indians 143
 
5.3%
super 141
 
5.3%
delhi 130
 
4.9%
chennai 122
 
4.6%
riders 122
 
4.6%
knight 122
 
4.6%
kolkata 122
 
4.6%
royal 121
 
4.5%
Other values (26) 1283
47.9%

Most occurring characters

ValueCountFrequency (%)
a 2323
 
11.9%
n 1619
 
8.3%
1585
 
8.1%
e 1362
 
7.0%
i 1354
 
6.9%
s 1318
 
6.8%
r 1061
 
5.4%
l 994
 
5.1%
h 666
 
3.4%
g 664
 
3.4%
Other values (28) 6579
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15175
77.7%
Uppercase Letter 2765
 
14.2%
Space Separator 1585
 
8.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2323
15.3%
n 1619
10.7%
e 1362
9.0%
i 1354
8.9%
s 1318
8.7%
r 1061
 
7.0%
l 994
 
6.6%
h 666
 
4.4%
g 664
 
4.4%
u 607
 
4.0%
Other values (12) 3207
21.1%
Uppercase Letter
ValueCountFrequency (%)
R 496
17.9%
K 491
17.8%
C 336
12.2%
D 253
9.2%
S 242
8.8%
I 228
8.2%
M 143
 
5.2%
P 142
 
5.1%
B 121
 
4.4%
H 88
 
3.2%
Other values (5) 225
8.1%
Space Separator
ValueCountFrequency (%)
1585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17940
91.9%
Common 1585
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2323
 
12.9%
n 1619
 
9.0%
e 1362
 
7.6%
i 1354
 
7.5%
s 1318
 
7.3%
r 1061
 
5.9%
l 994
 
5.5%
h 666
 
3.7%
g 664
 
3.7%
u 607
 
3.4%
Other values (27) 5972
33.3%
Common
ValueCountFrequency (%)
1585
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2323
 
11.9%
n 1619
 
8.3%
1585
 
8.1%
e 1362
 
7.0%
i 1354
 
6.9%
s 1318
 
6.8%
r 1061
 
5.4%
l 994
 
5.1%
h 666
 
3.4%
g 664
 
3.4%
Other values (28) 6579
33.7%

toss_decision
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size57.1 KiB
field
704 
bat
391 

Length

Max length5
Median length5
Mean length4.2858447
Min length3

Characters and Unicode

Total characters4693
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfield
2nd rowbat
3rd rowbat
4th rowbat
5th rowbat

Common Values

ValueCountFrequency (%)
field 704
64.3%
bat 391
35.7%

Length

2025-03-14T09:40:45.793901image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:40:45.860982image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
field 704
64.3%
bat 391
35.7%

Most occurring characters

ValueCountFrequency (%)
f 704
15.0%
i 704
15.0%
e 704
15.0%
l 704
15.0%
d 704
15.0%
b 391
8.3%
a 391
8.3%
t 391
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4693
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 704
15.0%
i 704
15.0%
e 704
15.0%
l 704
15.0%
d 704
15.0%
b 391
8.3%
a 391
8.3%
t 391
8.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 4693
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 704
15.0%
i 704
15.0%
e 704
15.0%
l 704
15.0%
d 704
15.0%
b 391
8.3%
a 391
8.3%
t 391
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4693
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 704
15.0%
i 704
15.0%
e 704
15.0%
l 704
15.0%
d 704
15.0%
b 391
8.3%
a 391
8.3%
t 391
8.3%

winner
Categorical

High correlation 

Distinct20
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size71.7 KiB
Mumbai Indians
144 
Chennai Super Kings
138 
Kolkata Knight Riders
131 
Royal Challengers Bangalore
116 
Rajasthan Royals
112 
Other values (15)
454 

Length

Max length27
Median length22
Mean length17.952511
Min length9

Characters and Unicode

Total characters19658
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKolkata Knight Riders
2nd rowChennai Super Kings
3rd rowDelhi Daredevils
4th rowRoyal Challengers Bangalore
5th rowKolkata Knight Riders

Common Values

ValueCountFrequency (%)
Mumbai Indians 144
13.2%
Chennai Super Kings 138
12.6%
Kolkata Knight Riders 131
12.0%
Royal Challengers Bangalore 116
10.6%
Rajasthan Royals 112
10.2%
Kings XI Punjab 88
8.0%
Sunrisers Hyderabad 88
8.0%
Delhi Daredevils 67
6.1%
Delhi Capitals 48
 
4.4%
Deccan Chargers 29
 
2.6%
Other values (10) 134
12.2%

Length

2025-03-14T09:40:45.934652image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 250
 
9.2%
super 162
 
6.0%
mumbai 144
 
5.3%
indians 144
 
5.3%
chennai 138
 
5.1%
kolkata 131
 
4.8%
knight 131
 
4.8%
riders 131
 
4.8%
challengers 123
 
4.5%
royal 123
 
4.5%
Other values (28) 1238
45.6%

Most occurring characters

ValueCountFrequency (%)
a 2303
 
11.7%
n 1688
 
8.6%
1620
 
8.2%
i 1389
 
7.1%
e 1342
 
6.8%
s 1301
 
6.6%
r 1037
 
5.3%
l 971
 
4.9%
g 686
 
3.5%
h 654
 
3.3%
Other values (29) 6667
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15240
77.5%
Uppercase Letter 2798
 
14.2%
Space Separator 1620
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2303
15.1%
n 1688
11.1%
i 1389
9.1%
e 1342
8.8%
s 1301
8.5%
r 1037
 
6.8%
l 971
 
6.4%
g 686
 
4.5%
h 654
 
4.3%
u 633
 
4.2%
Other values (12) 3236
21.2%
Uppercase Letter
ValueCountFrequency (%)
K 524
18.7%
R 493
17.6%
C 338
12.1%
S 265
9.5%
I 232
8.3%
D 211
7.5%
M 144
 
5.1%
P 139
 
5.0%
B 123
 
4.4%
X 88
 
3.1%
Other values (6) 241
8.6%
Space Separator
ValueCountFrequency (%)
1620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18038
91.8%
Common 1620
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2303
 
12.8%
n 1688
 
9.4%
i 1389
 
7.7%
e 1342
 
7.4%
s 1301
 
7.2%
r 1037
 
5.7%
l 971
 
5.4%
g 686
 
3.8%
h 654
 
3.6%
u 633
 
3.5%
Other values (28) 6034
33.5%
Common
ValueCountFrequency (%)
1620
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2303
 
11.7%
n 1688
 
8.6%
1620
 
8.2%
i 1389
 
7.1%
e 1342
 
6.8%
s 1301
 
6.6%
r 1037
 
5.3%
l 971
 
4.9%
g 686
 
3.5%
h 654
 
3.3%
Other values (29) 6667
33.9%

result
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size58.5 KiB
wickets
578 
runs
498 
tie
 
14
no result
 
5

Length

Max length9
Median length7
Mean length5.5936073
Min length3

Characters and Unicode

Total characters6125
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowruns
2nd rowruns
3rd rowwickets
4th rowwickets
5th rowwickets

Common Values

ValueCountFrequency (%)
wickets 578
52.8%
runs 498
45.5%
tie 14
 
1.3%
no result 5
 
0.5%

Length

2025-03-14T09:40:46.017575image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:40:46.082496image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
wickets 578
52.5%
runs 498
45.3%
tie 14
 
1.3%
no 5
 
0.5%
result 5
 
0.5%

Most occurring characters

ValueCountFrequency (%)
s 1081
17.6%
e 597
9.7%
t 597
9.7%
i 592
9.7%
w 578
9.4%
c 578
9.4%
k 578
9.4%
r 503
8.2%
u 503
8.2%
n 503
8.2%
Other values (3) 15
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6120
99.9%
Space Separator 5
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 1081
17.7%
e 597
9.8%
t 597
9.8%
i 592
9.7%
w 578
9.4%
c 578
9.4%
k 578
9.4%
r 503
8.2%
u 503
8.2%
n 503
8.2%
Other values (2) 10
 
0.2%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6120
99.9%
Common 5
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 1081
17.7%
e 597
9.8%
t 597
9.8%
i 592
9.7%
w 578
9.4%
c 578
9.4%
k 578
9.4%
r 503
8.2%
u 503
8.2%
n 503
8.2%
Other values (2) 10
 
0.2%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6125
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 1081
17.6%
e 597
9.7%
t 597
9.7%
i 592
9.7%
w 578
9.4%
c 578
9.4%
k 578
9.4%
r 503
8.2%
u 503
8.2%
n 503
8.2%
Other values (3) 15
 
0.2%

result_margin
Real number (ℝ)

Distinct99
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.942466
Minimum-1
Maximum146
Zeros0
Zeros (%)0.0%
Negative19
Negative (%)1.7%
Memory size8.7 KiB
2025-03-14T09:40:46.162291image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2
Q15
median8
Q319
95-th percentile65
Maximum146
Range147
Interquartile range (IQR)14

Descriptive statistics

Standard deviation21.728745
Coefficient of variation (CV)1.2825019
Kurtosis7.5661907
Mean16.942466
Median Absolute Deviation (MAD)3
Skewness2.5767867
Sum18552
Variance472.13837
MonotonicityNot monotonic
2025-03-14T09:40:46.252497image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 131
 
12.0%
7 130
 
11.9%
5 109
 
10.0%
8 85
 
7.8%
4 74
 
6.8%
9 59
 
5.4%
3 39
 
3.6%
10 33
 
3.0%
2 21
 
1.9%
-1 19
 
1.7%
Other values (89) 395
36.1%
ValueCountFrequency (%)
-1 19
 
1.7%
1 18
 
1.6%
2 21
 
1.9%
3 39
 
3.6%
4 74
6.8%
5 109
10.0%
6 131
12.0%
7 130
11.9%
8 85
7.8%
9 59
5.4%
ValueCountFrequency (%)
146 1
0.1%
144 1
0.1%
140 1
0.1%
138 1
0.1%
130 1
0.1%
118 1
0.1%
112 1
0.1%
111 1
0.1%
106 1
0.1%
105 1
0.1%

target_runs
Real number (ℝ)

Distinct171
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.23014
Minimum0
Maximum288
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-03-14T09:40:46.339766image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile111
Q1146
median166
Q3187
95-th percentile218.3
Maximum288
Range288
Interquartile range (IQR)41

Descriptive statistics

Standard deviation34.487313
Coefficient of variation (CV)0.2087229
Kurtosis1.9777639
Mean165.23014
Median Absolute Deviation (MAD)20
Skewness-0.48616158
Sum180927
Variance1189.3748
MonotonicityNot monotonic
2025-03-14T09:40:46.424848image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
172 21
 
1.9%
166 21
 
1.9%
165 20
 
1.8%
188 20
 
1.8%
163 19
 
1.7%
179 17
 
1.6%
164 17
 
1.6%
169 17
 
1.6%
155 16
 
1.5%
178 16
 
1.5%
Other values (161) 911
83.2%
ValueCountFrequency (%)
0 3
0.3%
43 1
 
0.1%
48 1
 
0.1%
52 1
 
0.1%
53 1
 
0.1%
54 1
 
0.1%
58 1
 
0.1%
61 1
 
0.1%
63 1
 
0.1%
66 1
 
0.1%
ValueCountFrequency (%)
288 1
0.1%
278 1
0.1%
273 1
0.1%
267 1
0.1%
264 1
0.1%
262 1
0.1%
258 2
0.2%
249 1
0.1%
247 1
0.1%
246 1
0.1%

target_overs
Real number (ℝ)

Distinct16
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.705205
Minimum0
Maximum20
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-03-14T09:40:46.498768image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q120
median20
Q320
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.8869998
Coefficient of variation (CV)0.095761486
Kurtosis56.691548
Mean19.705205
Median Absolute Deviation (MAD)0
Skewness-7.2656479
Sum21577.2
Variance3.5607681
MonotonicityNot monotonic
2025-03-14T09:40:46.570746image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
20 1062
97.0%
6 4
 
0.4%
16 3
 
0.3%
8 3
 
0.3%
10 3
 
0.3%
0 3
 
0.3%
12 3
 
0.3%
18 2
 
0.2%
17 2
 
0.2%
13 2
 
0.2%
Other values (6) 8
 
0.7%
ValueCountFrequency (%)
0 3
0.3%
5 2
0.2%
6 4
0.4%
8 3
0.3%
9 1
 
0.1%
9.2 1
 
0.1%
10 3
0.3%
11 2
0.2%
12 3
0.3%
13 2
0.2%
ValueCountFrequency (%)
20 1062
97.0%
18 2
 
0.2%
17 2
 
0.2%
16 3
 
0.3%
15 1
 
0.1%
14 1
 
0.1%
13 2
 
0.2%
12 3
 
0.3%
11 2
 
0.2%
10 3
 
0.3%
Distinct62
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size64.5 KiB
2025-03-14T09:40:46.786078image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length21
Median length16
Mean length11.221918
Min length5

Characters and Unicode

Total characters12288
Distinct characters48
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowAsad Rauf
2nd rowMR Benson
3rd rowAleem Dar
4th rowSJ Davis
5th rowBF Bowden
ValueCountFrequency (%)
ak 115
 
5.2%
chaudhary 115
 
5.2%
hdpk 79
 
3.6%
dharmasena 79
 
3.6%
s 77
 
3.5%
kn 59
 
2.7%
ananthapadmanabhan 59
 
2.7%
cb 53
 
2.4%
gaffaney 53
 
2.4%
asad 51
 
2.3%
Other values (109) 1480
66.7%
2025-03-14T09:40:47.116776image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1669
 
13.6%
1125
 
9.2%
n 929
 
7.6%
e 656
 
5.3%
h 642
 
5.2%
r 607
 
4.9%
d 516
 
4.2%
A 409
 
3.3%
K 377
 
3.1%
o 352
 
2.9%
Other values (38) 5006
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8036
65.4%
Uppercase Letter 3127
 
25.4%
Space Separator 1125
 
9.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1669
20.8%
n 929
11.6%
e 656
 
8.2%
h 642
 
8.0%
r 607
 
7.6%
d 516
 
6.4%
o 352
 
4.4%
s 341
 
4.2%
i 336
 
4.2%
u 284
 
3.5%
Other values (14) 1704
21.2%
Uppercase Letter
ValueCountFrequency (%)
A 409
13.1%
K 377
12.1%
D 322
10.3%
C 240
 
7.7%
B 234
 
7.5%
R 225
 
7.2%
N 216
 
6.9%
S 186
 
5.9%
M 145
 
4.6%
J 133
 
4.3%
Other values (13) 640
20.5%
Space Separator
ValueCountFrequency (%)
1125
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11163
90.8%
Common 1125
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1669
 
15.0%
n 929
 
8.3%
e 656
 
5.9%
h 642
 
5.8%
r 607
 
5.4%
d 516
 
4.6%
A 409
 
3.7%
K 377
 
3.4%
o 352
 
3.2%
s 341
 
3.1%
Other values (37) 4665
41.8%
Common
ValueCountFrequency (%)
1125
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1669
 
13.6%
1125
 
9.2%
n 929
 
7.6%
e 656
 
5.3%
h 642
 
5.2%
r 607
 
4.9%
d 516
 
4.2%
A 409
 
3.3%
K 377
 
3.1%
o 352
 
2.9%
Other values (38) 5006
40.7%
Distinct62
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
2025-03-14T09:40:47.401294image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length21
Median length16
Mean length10.311416
Min length5

Characters and Unicode

Total characters11291
Distinct characters49
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.5%

Sample

1st rowRE Koertzen
2nd rowSL Shastri
3rd rowGA Pratapkumar
4th rowDJ Harper
5th rowK Hariharan
ValueCountFrequency (%)
s 99
 
4.5%
ravi 83
 
3.7%
sharma 79
 
3.6%
vk 61
 
2.7%
c 60
 
2.7%
shamshuddin 60
 
2.7%
nitin 54
 
2.4%
menon 54
 
2.4%
sja 54
 
2.4%
taufel 54
 
2.4%
Other values (108) 1566
70.4%
2025-03-14T09:40:47.840425image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1310
 
11.6%
1129
 
10.0%
n 791
 
7.0%
r 599
 
5.3%
i 569
 
5.0%
e 550
 
4.9%
h 516
 
4.6%
S 461
 
4.1%
d 400
 
3.5%
K 334
 
3.0%
Other values (39) 4632
41.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7069
62.6%
Uppercase Letter 3093
27.4%
Space Separator 1129
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1310
18.5%
n 791
11.2%
r 599
 
8.5%
i 569
 
8.0%
e 550
 
7.8%
h 516
 
7.3%
d 400
 
5.7%
u 288
 
4.1%
o 247
 
3.5%
l 238
 
3.4%
Other values (15) 1561
22.1%
Uppercase Letter
ValueCountFrequency (%)
S 461
14.9%
K 334
10.8%
R 288
 
9.3%
A 231
 
7.5%
N 202
 
6.5%
J 191
 
6.2%
T 186
 
6.0%
C 180
 
5.8%
V 159
 
5.1%
B 134
 
4.3%
Other values (13) 727
23.5%
Space Separator
ValueCountFrequency (%)
1129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10162
90.0%
Common 1129
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1310
 
12.9%
n 791
 
7.8%
r 599
 
5.9%
i 569
 
5.6%
e 550
 
5.4%
h 516
 
5.1%
S 461
 
4.5%
d 400
 
3.9%
K 334
 
3.3%
R 288
 
2.8%
Other values (38) 4344
42.7%
Common
ValueCountFrequency (%)
1129
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11291
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1310
 
11.6%
1129
 
10.0%
n 791
 
7.0%
r 599
 
5.3%
i 569
 
5.0%
e 550
 
4.9%
h 516
 
4.6%
S 461
 
4.1%
d 400
 
3.5%
K 334
 
3.0%
Other values (39) 4632
41.0%

batting_avg
Real number (ℝ)

High correlation  Missing 

Distinct251
Distinct (%)23.5%
Missing26
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean18.4629
Minimum0.03
Maximum39.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-03-14T09:40:47.974387image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile0.5
Q15.11
median21.46
Q327.53
95-th percentile35.44
Maximum39.34
Range39.31
Interquartile range (IQR)22.42

Descriptive statistics

Standard deviation11.94888
Coefficient of variation (CV)0.64718329
Kurtosis-1.2869203
Mean18.4629
Median Absolute Deviation (MAD)9.02
Skewness-0.26731063
Sum19736.84
Variance142.77574
MonotonicityNot monotonic
2025-03-14T09:40:48.117285image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.48 25
 
2.3%
35.44 22
 
2.0%
26.61 19
 
1.7%
30.71 18
 
1.6%
36.31 18
 
1.6%
21.46 18
 
1.6%
24.28 17
 
1.6%
26.76 16
 
1.5%
0.47 16
 
1.5%
20.01 16
 
1.5%
Other values (241) 884
80.7%
(Missing) 26
 
2.4%
ValueCountFrequency (%)
0.03 2
 
0.2%
0.09 1
 
0.1%
0.12 2
 
0.2%
0.14 1
 
0.1%
0.18 1
 
0.1%
0.24 2
 
0.2%
0.25 1
 
0.1%
0.26 2
 
0.2%
0.27 3
0.3%
0.28 6
0.5%
ValueCountFrequency (%)
39.34 13
1.2%
37.21 3
 
0.3%
36.73 2
 
0.2%
36.31 18
1.6%
36.07 9
0.8%
36.06 2
 
0.2%
36 4
 
0.4%
35.44 22
2.0%
34.96 13
1.2%
34.59 4
 
0.4%

runs
Real number (ℝ)

High correlation  Missing 

Distinct233
Distinct (%)21.8%
Missing26
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean2032.6548
Minimum1
Maximum6634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-03-14T09:40:48.238248image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile26
Q1251
median1588
Q33222
95-th percentile5881
Maximum6634
Range6633
Interquartile range (IQR)2971

Descriptive statistics

Standard deviation1902.9034
Coefficient of variation (CV)0.93616651
Kurtosis-0.54382343
Mean2032.6548
Median Absolute Deviation (MAD)1358
Skewness0.75479155
Sum2172908
Variance3621041.3
MonotonicityNot monotonic
2025-03-14T09:40:48.350732image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5181 25
 
2.3%
4997 22
 
2.0%
5881 19
 
1.7%
5883 18
 
1.6%
6634 18
 
1.6%
4978 17
 
1.6%
2502 16
 
1.5%
3880 16
 
1.5%
3222 16
 
1.5%
2039 15
 
1.4%
Other values (223) 887
81.0%
(Missing) 26
 
2.4%
ValueCountFrequency (%)
1 5
0.5%
2 3
0.3%
4 7
0.6%
5 3
0.3%
6 2
 
0.2%
7 1
 
0.1%
8 3
0.3%
9 1
 
0.1%
10 1
 
0.1%
11 2
 
0.2%
ValueCountFrequency (%)
6634 18
1.6%
6244 12
1.1%
5883 18
1.6%
5881 19
1.7%
5536 14
1.3%
5181 25
2.3%
4997 22
2.0%
4978 17
1.6%
4954 7
 
0.6%
4377 7
 
0.6%

bowling_avg
Real number (ℝ)

Missing  Zeros 

Distinct194
Distinct (%)18.1%
Missing26
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean15558.427
Minimum0
Maximum760000
Zeros303
Zeros (%)27.7%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-03-14T09:40:48.446892image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median27.34
Q334.36
95-th percentile70000
Maximum760000
Range760000
Interquartile range (IQR)34.36

Descriptive statistics

Standard deviation76343.974
Coefficient of variation (CV)4.9069212
Kurtosis41.535767
Mean15558.427
Median Absolute Deviation (MAD)9.66
Skewness6.231224
Sum16631958
Variance5.8284024 × 109
MonotonicityNot monotonic
2025-03-14T09:40:48.536585image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 303
27.7%
41.94 22
 
2.0%
25.7 19
 
1.7%
30.8 19
 
1.7%
92.75 18
 
1.6%
20000 18
 
1.6%
29.8 16
 
1.5%
34.36 16
 
1.5%
31.17 16
 
1.5%
24.88 15
 
1.4%
Other values (184) 607
55.4%
(Missing) 26
 
2.4%
ValueCountFrequency (%)
0 303
27.7%
5 13
 
1.2%
10.8 1
 
0.1%
12.5 2
 
0.2%
14.43 1
 
0.1%
16.5 1
 
0.1%
17.76 1
 
0.1%
18 13
 
1.2%
18.24 6
 
0.5%
19.19 2
 
0.2%
ValueCountFrequency (%)
760000 1
 
0.1%
590000 8
0.7%
500000 1
 
0.1%
490000 6
0.5%
310000 6
0.5%
260000 3
 
0.3%
240000 3
 
0.3%
200000 1
 
0.1%
160000 10
0.9%
120000 4
 
0.4%

wickets
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct87
Distinct (%)8.1%
Missing26
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean37.118803
Minimum0
Maximum183
Zeros382
Zeros (%)34.9%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-03-14T09:40:48.651900image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13
Q361
95-th percentile152
Maximum183
Range183
Interquartile range (IQR)61

Descriptive statistics

Standard deviation49.01685
Coefficient of variation (CV)1.3205396
Kurtosis0.56343494
Mean37.118803
Median Absolute Deviation (MAD)13
Skewness1.3042883
Sum39680
Variance2402.6516
MonotonicityNot monotonic
2025-03-14T09:40:48.740610image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 382
34.9%
4 36
 
3.3%
1 36
 
3.3%
92 27
 
2.5%
42 26
 
2.4%
18 23
 
2.1%
15 20
 
1.8%
28 19
 
1.7%
166 18
 
1.6%
7 18
 
1.6%
Other values (77) 464
42.4%
(Missing) 26
 
2.4%
ValueCountFrequency (%)
0 382
34.9%
1 36
 
3.3%
2 4
 
0.4%
3 4
 
0.4%
4 36
 
3.3%
5 5
 
0.5%
6 14
 
1.3%
7 18
 
1.6%
8 3
 
0.3%
9 9
 
0.8%
ValueCountFrequency (%)
183 4
 
0.4%
170 6
 
0.5%
166 18
1.6%
157 9
0.8%
154 6
 
0.5%
152 15
1.4%
150 8
0.7%
148 10
0.9%
135 10
0.9%
132 16
1.5%

Interactions

2025-03-14T09:40:43.725283image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:40.680255image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:41.225466image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:41.825816image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:42.303517image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:42.780284image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:43.240963image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:43.792138image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:40.756160image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:41.292092image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:41.895889image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:42.374153image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:42.846697image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:43.311189image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:43.854923image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:40.822842image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:41.355184image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:41.962692image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:42.438525image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:42.908281image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:43.377727image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:43.923244image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:40.893827image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:41.423203image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:42.032767image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:42.507793image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:42.974651image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:43.448385image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:43.991841image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:40.962898image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:41.631676image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:42.103372image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:42.575791image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:43.042989image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:43.520883image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:44.059202image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:41.028318image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:41.695121image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:42.168143image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:42.642935image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:43.104424image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:43.588935image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:44.134240image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:41.147460image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:41.761468image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:42.237022image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:42.713321image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:43.173202image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-14T09:40:43.657585image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2025-03-14T09:40:48.813553image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
batting_avgbowling_avgmatch_typeresultresult_marginrunstarget_overstarget_runsteam1team2toss_decisiontoss_winnerwicketswinner
batting_avg1.000-0.0890.0000.0000.0880.8060.0020.3220.1030.1150.0590.104-0.6840.224
bowling_avg-0.0891.0000.0690.0000.0250.013-0.040-0.0760.0320.0450.0000.0270.2940.137
match_type0.0000.0691.0000.0000.0000.0000.0000.0000.0520.0410.0470.0000.0000.000
result0.0000.0000.0001.0000.4170.0000.4550.4990.0410.0310.0000.0000.0000.574
result_margin0.0880.0250.0000.4171.0000.0640.0170.3050.0000.0000.0000.041-0.0590.000
runs0.8060.0130.0000.0000.0641.0000.0070.2580.1230.1020.0150.117-0.4420.244
target_overs0.002-0.0400.0000.4550.0170.0071.0000.2250.0640.0000.0000.1170.0020.310
target_runs0.322-0.0760.0000.4990.3050.2580.2251.0000.0580.0820.1300.106-0.2740.267
team10.1030.0320.0520.0410.0000.1230.0640.0581.0000.0760.1860.3820.1010.459
team20.1150.0450.0410.0310.0000.1020.0000.0820.0761.0000.0960.5990.0640.464
toss_decision0.0590.0000.0470.0000.0000.0150.0000.1300.1860.0961.0000.1400.0000.145
toss_winner0.1040.0270.0000.0000.0410.1170.1170.1060.3820.5990.1401.0000.0840.489
wickets-0.6840.2940.0000.000-0.059-0.4420.002-0.2740.1010.0640.0000.0841.0000.182
winner0.2240.1370.0000.5740.0000.2440.3100.2670.4590.4640.1450.4890.1821.000

Missing values

2025-03-14T09:40:44.235363image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-14T09:40:44.388995image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-14T09:40:44.497005image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

match_typevenueteam1team2toss_winnertoss_decisionwinnerresultresult_margintarget_runstarget_oversumpire1umpire2batting_avgrunsbowling_avgwickets
0LeagueM Chinnaswamy StadiumRoyal Challengers BangaloreKolkata Knight RidersRoyal Challengers BangalorefieldKolkata Knight Ridersruns140.0223.020.0Asad RaufRE Koertzen26.442882.00.000.0
1LeaguePunjab Cricket Association Stadium, MohaliKings XI PunjabChennai Super KingsChennai Super KingsbatChennai Super Kingsruns33.0241.020.0MR BensonSL Shastri34.091977.00.000.0
2LeagueFeroz Shah KotlaDelhi DaredevilsRajasthan RoyalsRajasthan RoyalsbatDelhi Daredevilswickets9.0130.020.0Aleem DarGA Pratapkumar8.85177.019.7027.0
3LeagueWankhede StadiumMumbai IndiansRoyal Challengers BangaloreMumbai IndiansbatRoyal Challengers Bangalorewickets5.0166.020.0SJ DavisDJ Harper17.13394.00.000.0
4LeagueEden GardensKolkata Knight RidersDeccan ChargersDeccan ChargersbatKolkata Knight Riderswickets5.0111.020.0BF BowdenK Hariharan21.671322.060.628.0
5LeagueSawai Mansingh StadiumRajasthan RoyalsKings XI PunjabKings XI PunjabbatRajasthan Royalswickets6.0167.020.0Aleem DarRB Tiffin26.763880.029.8092.0
6LeagueRajiv Gandhi International Stadium, UppalDeccan ChargersDelhi DaredevilsDeccan ChargersbatDelhi Daredevilswickets9.0143.020.0IL HowellAM Saheba26.232728.039.336.0
7LeagueMA Chidambaram Stadium, ChepaukChennai Super KingsMumbai IndiansMumbai IndiansfieldChennai Super Kingsruns6.0209.020.0DJ HarperGA Pratapkumar34.591107.00.000.0
8LeagueRajiv Gandhi International Stadium, UppalDeccan ChargersRajasthan RoyalsRajasthan RoyalsfieldRajasthan Royalswickets3.0215.020.0Asad RaufMR Benson20.013222.034.3642.0
9LeaguePunjab Cricket Association Stadium, MohaliKings XI PunjabMumbai IndiansMumbai IndiansfieldKings XI Punjabruns66.0183.020.0Aleem DarAM Saheba24.811687.00.000.0
match_typevenueteam1team2toss_winnertoss_decisionwinnerresultresult_margintarget_runstarget_oversumpire1umpire2batting_avgrunsbowling_avgwickets
1085LeagueM Chinnaswamy Stadium, BengaluruRoyal Challengers BengaluruDelhi CapitalsDelhi CapitalsfieldRoyal Challengers Bengalururuns47.0188.020.0A Nand KishoreVA KulkarniNaNNaNNaNNaN
1086LeagueArun Jaitley Stadium, DelhiDelhi CapitalsLucknow Super GiantsLucknow Super GiantsfieldDelhi Capitalsruns19.0209.020.0A TotreVinod Seshan0.5955.038.6072.0
1087LeagueBarsapara Cricket Stadium, GuwahatiRajasthan RoyalsPunjab KingsRajasthan RoyalsbatPunjab Kingswickets5.0145.020.0R PanditMV Saidharshan Kumar10.53337.031.4132.0
1088LeagueWankhede Stadium, MumbaiLucknow Super GiantsMumbai IndiansMumbai IndiansfieldLucknow Super Giantsruns18.0215.020.0Navdeep SinghR Pandit21.21912.00.000.0
1089LeagueM Chinnaswamy Stadium, BengaluruRoyal Challengers BengaluruChennai Super KingsChennai Super KingsfieldRoyal Challengers Bengalururuns27.0219.020.0A TotreKN Ananthapadmanabhan31.223403.0160000.000.0
1090LeagueRajiv Gandhi International Stadium, Uppal, HyderabadPunjab KingsSunrisers HyderabadPunjab KingsbatSunrisers Hyderabadwickets4.0215.020.0Nitin MenonVK Sharma18.53667.031.147.0
1091QualifierNarendra Modi Stadium, AhmedabadSunrisers HyderabadKolkata Knight RidersSunrisers HyderabadbatKolkata Knight Riderswickets8.0160.020.0AK ChaudharyR Pandit3.6996.021.3234.0
1092EliminatorNarendra Modi Stadium, AhmedabadRoyal Challengers BengaluruRajasthan RoyalsRajasthan RoyalsfieldRajasthan Royalswickets4.0173.020.0KN AnanthapadmanabhanMV Saidharshan Kumar3.57647.029.27157.0
1093QualifierMA Chidambaram Stadium, Chepauk, ChennaiSunrisers HyderabadRajasthan RoyalsRajasthan RoyalsfieldSunrisers Hyderabadruns36.0176.020.0Nitin MenonVK Sharma10.73279.036.7713.0
1094FinalMA Chidambaram Stadium, Chepauk, ChennaiSunrisers HyderabadKolkata Knight RidersSunrisers HyderabadbatKolkata Knight Riderswickets8.0114.020.0J MadanagopalNitin Menon3.6996.021.3234.0